Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 209; p. 116433
• Main Authors: Bolton, Thomas A.W., Kebets, Valeria, Glerean, Enrico, Zöller, Daniela, Li, Jingwei, Yeo, B.T. Thomas, Caballero-Gaudes, César, Van De Ville, Dimitri
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-04-2020; Elsevier Limited; Elsevier
• Subjects: Adult; Anthropometry; Behavior - physiology; Behaviour; Bias; Body mass index; Brain - diagnostic imaging; Brain - physiology; Cognitive ability; Connectome; Functional magnetic resonance imaging; Head Movements - physiology; Humans; Impulsivity; Magnetic Resonance Imaging; Motion artefacts; Multivariate analysis; Neuroimaging; Partial least squares analysis; Personality - physiology; Resting-state fMRI; Spatio-temporal motion; Resting-state fMRI; Behaviour; Partial least squares analysis; Motion artefacts; Spatio-temporal motion
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116433

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses.
Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics.
Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data.
Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.
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